Segmentation of ground-based laser scanning points from urban environment

ABSTRACT

A method, apparatus, system, and article of manufacture provide object descriptors for objects in point cloud data for an urban environment by segmenting the point cloud data. Point cloud data for an urban environment is obtained using a ground-based laser scanner. Terrain points are filtered out from the point cloud data using ground filtering. The point cloud data is then segmented into two or more blocks. Objects that lie on neighboring adjacent blocks are combined. Object descriptors for the combined objects are then provided (e.g., to the user or a program used by the user).

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is related to the following co-pending andcommonly-assigned patent application, which application is incorporatedby reference herein:

U.S. patent application Ser. No. 12/849,647, entitled “PIPERECONSTRUCTION FROM UNORGANIZED POINT CLOUD DATA”, by Yan Fu, XiaofengZhu, Jin Yang, and Zhenggang Yuan, filed on Aug. 3, 2010, whichapplication claims priority to Provisional Application Ser. No.61/353,486, filed Jun. 10, 2010, by Yan Fu, Xiaofeng Zhu, Jin Yang, andZhenggang Yuan, entitled “PIPE RECONSTRUCTION FROM UNORGANIZED POINTCLOUD DATA”; and

U.S. patent application Ser. No. 12/849,670, entitled “PRIMITIVE QUADRICSURFACE EXTRACTION FROM UNORGANIZED POINT CLOUD DATA”, by Yan Fu,Xiaofeng Zhu, Jin Yang, and Zhenggang Yuan, filed on Aug. 3, 2010, whichapplication claims priority to Provisional Application Ser. No.61/353,492, filed Jun. 10, 2010, by Yan Fu, Jin Yang, Xiaofeng Zhu, andZhenggang Yuan, entitled “PRIMITIVE QUADRIC SURFACE EXTRACTION FROMUNORGANIZED POINT CLOUD DATA”.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates generally to point cloud data, and inparticular, to a method, apparatus, and article of manufacture forautomatically segmenting point cloud data from an urban environment intoobjects.

2. Description of the Related Art

(Note: This application references a number of different publications asindicated throughout the specification by references enclosed inbrackets e.g., [x]. Such references may indicate the first named authorsand year of publication e.g., [Jones 2002]. A list of these differentpublications ordered according to these references can be found below inthe section entitled “References.” Each of these publications isincorporated by reference herein.)

Laser scanner equipment is increasingly employed for surveying and urbanplanning, etc. The data acquired by ground-based laser scanners isextensive and dense. Further, the data (referred to as point-cloud data)is difficult to interpret especially when no color or intensityinformation is available. Segmentation is a critical pre-processing stepin the interpretation of the scanned environment. In this regard,segmentation refers to the operation that separates points intodifferent groups based on spatial characteristics without knowing whatthe groups really are. Segmentation is used to identify the object shapeand then extract the shape descriptors. Segmentation results in asemantic description of the environment that can be used to supporthigh-level decisions. Moreover, the quality of a classification of anobject can also be improved based on segmentation results. To betterunderstand segmentation, a description of the prior art and related workmay be useful.

When a laser scanner is used to scan a scene/urban environment, thescene often contains many objects. It is difficult to determine whichpoints belong to which objects in the scene. For example, if a cityblock is laser scanned, in addition to an office building, there may bemany objects such as trees, vegetation, roads, etc. It is desirable topick out which points belong to the building and which points belong tothe vegetation, to the ground, and to other roads. Segmentation isperformed to separate the points into various groups/objects in order toclassify the points. Various methods have been attempted in the priorart to quickly and efficiently segment laser-scanned data. However, mostof the prior art techniques are problematic with large ground basedcomplex streets/scenes.

The topic of point cloud segmentation has been researched for severalyears. In some scenarios, segmentation is performed on a point cloudscanned from a single object and the goal is to decompose the pointcloud into separate surface patches. Embodiments of the presentinvention only focus on the related works of segmentation of pointclouds from an urban environment. Urban environments are usually verycomplex and consist of many objects. Further, the surfaces of theobjects are not smooth and it is difficult to estimate the differentialgeometric properties. In view of such properties, it can be seen thatother segmentation methods are quite different from the surface patchsegmentation of point clouds.

Some researchers segment three-dimensional (3D) laser point cloud basedon a graph cut method such as a normalized cut and minimum (min) cut[Boykov 2006]. Aleksey et al. [Alkeskey 2009] segmented the foregroundpoints from the background points by a min cut method on a 3D graphbuilt on a nearest neighbor graph. Such a method requires priorknowledge of the location of the objects to be segmented. Zhu et. al[Zhu 2010] also proposed a graph-based segmentation of range images inan urban environment. However, Zhu's method requires the removal ofground points beforehand.

Another category of segmentation approaches (other than the graph-basedsegmentation) are focused on the explicit modeling of surfacediscontinuity. Melkumyan [Melkumyan 2008] first built a 3D mesh from thepoint cloud data and then defined the discontinuity based on the longedge and acute angle of the mesh. However, the mesh reconstruction oflaser scanning data from an outdoor environment is not trivial initself. Moonsmann [Moonsmann 2009] segments ground and objects from 3DLiDAR (light detection and ranging) scans based on local convexitymeasures. Although such a method is fast and may show good results insome kind of urban environments, it is not general enough to handle thecases with overhanging structures.

Segmentation approaches based on statistics are also explored byHernandez and Marcotegui [Hernandez 2009]. The 3D points are projectedto a horizontal plane. The number of points projected to the same cellis accumulated to form an accumulated image and the maximal height ineach cell is extracted to form a range image. However, this methodassumes that the principal objects in the scene are facade buildings andthat the ground data is perpendicular to facade data. Accordingly, suchan approach is not general for other environments.

In view of the above, what is needed is the ability to segment pointcloud data of an urban environment scene in order to remove extraneousobjects and separate the point cloud data into individual objects forfurther processing.

SUMMARY OF THE INVENTION

Segmentation of laser scanning data provides an object descriptor of thescanned environment, which facilitates the scene interpretation andurban environment management. Embodiments of the invention provide amethod to segment the point cloud data from ground-based laser scanners.Ground filtering is used in a preprocess phase to filter out most of theterrain points. Afterwards, the remaining terrain points are furtherextracted based on the local terrain surface property. Non-ground pointsabove the terrain are clustered into individual objects based on cellcontinuity. Measurements of cell continuity can be adaptively adjustedto prevent over-segmentation problems. To handle large scenes of anurban environment, block-based segmentation may be used and then theobjects in the different blocks may be combined. Segmentation of pointsin different blocks can be also parallelized to improve time efficiency.

BRIEF DESCRIPTION OF THE DRAWINGS

Referring now to the drawings in which like reference numbers representcorresponding parts throughout:

FIG. 1 is an exemplary hardware and software environment used toimplement one or more embodiments of the invention;

FIG. 2 schematically illustrates a typical distributed computer systemusing a network to connect client computers to server computers inaccordance with one or more embodiments of the invention;

FIG. 3 illustrates the overall logical flow for performing segmentationin accordance with one or more embodiments of the invention;

FIG. 4 illustrates the logical flow for eliminating non-ground points inaccordance with one or more embodiments of the invention;

FIG. 5 is a flow chart illustrating an additional ground point removalprocess in accordance with one or more embodiments of the invention;

FIG. 6 illustrates the logical flow for detecting a ground cell inaccordance with one or more embodiments of the invention;

FIG. 7 illustrates a diagram of empty, non-empty, non-ground, andpotential ground cells in accordance with the flow chart of FIG. 6 inaccordance with one or more embodiments of the invention;

FIG. 8 illustrates the logical flow for conducting ground cellpropagation in accordance with one or more embodiments of the invention;

FIGS. 9A and 9B illustrate merge objects lying on adjacent boundaryfaces of neighboring blocks in accordance with one or more embodimentsof the invention;

FIGS. 10A and 10B illustrate merge objects lying on diagonal cornercolumns of neighboring blocks in accordance with one or more embodimentsof the invention; and

FIGS. 11A and 11B illustrate an example of the value change of array Mduring an object combination process in accordance with one or moreembodiments of the invention.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

In the following description, reference is made to the accompanyingdrawings which form a part hereof, and which is shown, by way ofillustration, several embodiments of the present invention. It isunderstood that other embodiments may be utilized and structural changesmay be made without departing from the scope of the present invention.

Overview

Embodiments of the invention provide a system for automaticallysegmenting point cloud data of 3D scans from an urban environment intosmall objects. The segmentation methodology is mainly based on theadjacency of object components. To remove the link of objects caused byterrain points, ground filtering is used to filter out most of theterrain points in a preprocess phase. Afterwards, the remaining terrainpoints are further extracted based on the local terrain surfaceproperty. After the connection through terrain is fully eliminated,non-ground points above the terrain are clustered into individualobjects based on cell continuity. Measurement of cell continuity can beadaptively adjusted to prevent over-segmentation problems. Due to thelarge scale and density of laser scanned points from an urbanenvironment, significant memory is consumed during the segmentationprocess. Accordingly, embodiments of the invention subdivide the wholescan into small blocks and then handle each block individually. Theresulting small patches are then combined together to obtain the finalsegmentation. This approach not only dramatically reduces the memoryrequirement but also facilitates parallelization.

Hardware Environment

FIG. 1 is an exemplary hardware and software environment 100 used toimplement one or more embodiments of the invention. The hardware andsoftware environment includes a computer 102 and may includeperipherals. Computer 102 may be a user/client computer, servercomputer, or may be a database computer. The computer 102 comprises ageneral purpose hardware processor 104A and/or a special purposehardware processor 104B (hereinafter alternatively collectively referredto as processor 104) and a memory 106, such as random access memory(RAM). The computer 102 may be coupled to, and/or integrated with, otherdevices, including input/output (I/O) devices such as a keyboard 114, acursor control device 116 (e.g., a mouse, a pointing device, pen andtablet, touch screen, multi-touch device, etc.) and a printer 128. Inone or more embodiments, computer 102 may be coupled to, or maycomprise, a portable or media viewing/listening device 132 (e.g., an MP3player, iPod™, Nook™, portable digital video player, cellular device,personal digital assistant, etc.). In yet another embodiment, thecomputer 102 may comprise a multi-touch device, mobile phone, gamingsystem, internet enabled television, television set top box, or otherinternet enabled device executing on various platforms and operatingsystems.

In one or more embodiments, computer 102 may be coupled to, and/orintegrated with, a laser scanning device 134. Such a laser scanningdevice 134 is configured to scan an object or urban environment andobtain a digital representative of such an object/environment in theform of point cloud data that may be processed by the computer 102.

In one embodiment, the computer 102 operates by the general purposeprocessor 104A performing instructions defined by the computer program110 under control of an operating system 108. The computer program 110and/or the operating system 108 may be stored in the memory 106 and mayinterface with the user and/or other devices to accept input andcommands and, based on such input and commands and the instructionsdefined by the computer program 110 and operating system 108, to provideoutput and results.

Output/results may be presented on the display 122 or provided toanother device for presentation or further processing or action. In oneembodiment, the display 122 comprises a liquid crystal display (LCD)having a plurality of separately addressable liquid crystals.Alternatively, the display 122 may comprise a light emitting diode (LED)display having clusters of red, green and blue diodes driven together toform full-color pixels. Each liquid crystal or pixel of the display 122changes to an opaque or translucent state to form a part of the image onthe display in response to the data or information generated by theprocessor 104 from the application of the instructions of the computerprogram 110 and/or operating system 108 to the input and commands. Theimage may be provided through a graphical user interface (GUI) module118A. Although the GUI module 118A is depicted as a separate module, theinstructions performing the GUI functions can be resident or distributedin the operating system 108, the computer program 110, or implementedwith special purpose memory and processors.

In one or more embodiments, the display 122 is integrated with/into thecomputer 102 and comprises a multi-touch device having a touch sensingsurface (e.g., track pod or touch screen) with the ability to recognizethe presence of two or more points of contact with the surface. Examplesof multi-touch devices include mobile devices (e.g., iPhone™, Nexus S™,Droid™ devices, etc.), tablet computers (e.g., iPad™, HP Touchpad™),portable/handheld game/music/video player/console devices (e.g., iPodTouch™, MP3 players, Nintendo 3DS™, PlayStation Portable™, etc.), touchtables, and walls (e.g., where an image is projected through acrylicand/or glass, and the image is then backlit with LEDs).

Some or all of the operations performed by the computer 102 according tothe computer program 110 instructions may be implemented in a specialpurpose processor 104B. In this embodiment, the some or all of thecomputer program 110 instructions may be implemented via firmwareinstructions stored in a read only memory (ROM), a programmable readonly memory (PROM) or flash memory within the special purpose processor104B or in memory 106. The special purpose processor 104B may also behardwired through circuit design to perform some or all of theoperations to implement the present invention. Further, the specialpurpose processor 104B may be a hybrid processor, which includesdedicated circuitry for performing a subset of functions, and othercircuits for performing more general functions such as responding tocomputer program instructions. In one embodiment, the special purposeprocessor is an application specific integrated circuit (ASIC).

The computer 102 may also implement a compiler 112 that allows anapplication program 110 written in a programming language such as COBOL,Pascal, C++, FORTRAN, or other language to be translated into processor104 readable code. Alternatively, the compiler 112 may be an interpreterthat executes instructions/source code directly, translates source codeinto an intermediate representation that is executed, or that executesstored precompiled code. Such source code may be written in a variety ofprogramming languages such as Java™, Perl™, Basic™, etc. Aftercompletion, the application or computer program 110 accesses andmanipulates data accepted from I/O devices and stored in the memory 106of the computer 102 using the relationships and logic that weregenerated using the compiler 112.

The computer 102 also optionally comprises an external communicationdevice such as a modem, satellite link, Ethernet card, or other devicefor accepting input from, and providing output to, other computers 102.

In one embodiment, instructions implementing the operating system 108,the computer program 110, and the compiler 112 are tangibly embodied ina non-transient computer-readable medium, e.g., data storage device 120,which could include one or more fixed or removable data storage devices,such as a zip drive, floppy disc drive 124, hard drive, CD-ROM drive,tape drive, etc. Further, the operating system 108 and the computerprogram 110 are comprised of computer program instructions which, whenaccessed, read and executed by the computer 102, cause the computer 102to perform the steps necessary to implement and/or use the present,invention or to load the program of instructions into a memory, thuscreating a special purpose data structure causing the computer tooperate as a specially programmed computer executing the method stepsdescribed herein. Computer program 110 and/or operating instructions mayalso be tangibly embodied in memory 106 and/or data communicationsdevices 130, thereby making a computer program product or article ofmanufacture according to the invention. As such, the terms “article ofmanufacture,” “program storage device,” and “computer program product,”as used herein, are intended to encompass a computer program accessiblefrom any computer readable device or media.

Of course, those skilled in the art will recognize that any combinationof the above components, or any number of different components,peripherals, and other devices, may be used with the computer 102.

FIG. 2 schematically illustrates a typical distributed computer system200 using a network 202 to connect client computers 102 to servercomputers 206. A typical combination of resources may include a network202 comprising the Internet, LANs (local area networks), WANs (wide areanetworks), SNA (systems network architecture) networks, or the like,clients 102 that are personal computers or workstations, and servers 206that are personal computers, workstations, minicomputers, or mainframes(as set forth in FIG. 1). However, it may be noted that differentnetworks such as a cellular network (e.g., GSM [global system for mobilecommunications] or otherwise), a satellite based network, or any othertype of network may be used to connect clients 102 and servers 206 inaccordance with embodiments of the invention.

A network 202 such as the Internet connects clients 102 to servercomputers 206. Network 202 may utilize ethernet, coaxial cable, wirelesscommunications, radio frequency (RF), etc. to connect and provide thecommunication between clients 102 and servers 206. Clients 102 mayexecute a client application or web browser and communicate with servercomputers 206 executing web servers 210. Such a web browser is typicallya program such as MICROSOFT INTERNET EXPLORER™, MOZILLA FIREFOX™,OPERA™, APPLE SAFARI™, etc. Further, the software executing on clients102 may be downloaded from server computer 206 to client computers 102and installed as a plug-in or ACTIVEX™ control of a web browser.Accordingly, clients 102 may utilize ACTIVEX™ components/componentobject model (COM) or distributed COM (DCOM) components to provide auser interface on a display of client 102. The web server 210 istypically a program such as MICROSOFT'S INTERNET INFORMATION SERVER™.

Web server 210 may host an Active Server Page (ASP) or Internet ServerApplication Programming Interface (ISAPI) application 212, which may beexecuting scripts. The scripts invoke objects that execute businesslogic (referred to as business objects). The business objects thenmanipulate data in database 216 through a database management system(DBMS) 214. Alternatively, database 216 may be part of, or connecteddirectly to, client 102 instead of communicating/obtaining theinformation from database 216 across network 202. When a developerencapsulates the business functionality into objects, the system may bereferred to as a component object model (COM) system. Accordingly, thescripts executing on web server 210 (and/or application 212) invoke COMobjects that implement the business logic. Further, server 206 mayutilize MICROSOFT'S™ Transaction Server (MTS) to access required datastored in database 216 via an interface such as ADO (Active DataObjects), OLE DB (Object Linking and Embedding DataBase), or ODBC (OpenDataBase Connectivity).

Generally, these components 200-216 all comprise logic and/or data thatis embodied in/or retrievable from device, medium, signal, or carrier,e.g., a data storage device, a data communications device, a remotecomputer or device coupled to the computer via a network or via anotherdata communications device, etc. Moreover, this logic and/or data, whenread, executed, and/or interpreted, results in the steps necessary toimplement and/or use the present invention being performed.

Although the terms “user computer”, “client computer”, and/or “servercomputer” are referred to herein, it is understood that such computers102 and 206 may be interchangeable and may further include thin clientdevices with limited or full processing capabilities, portable devicessuch as cell phones, notebook computers, pocket computers, multi-touchdevices, and/or any other devices with suitable processing,communication, and input/output capability.

Of course, those skilled in the art will recognize that any combinationof the above components, or any number of different components,peripherals, and other devices, may be used with computers 102 and 206.

Software Embodiment Overview

Embodiments of the invention are implemented as a software applicationon a client 102 or server computer 206. Further, as described above, theclient 102 or server computer 206 may comprise a thin client device or aportable device that has a multi-touch-based display and that maycomprise (or may be coupled to or receive data from) a 3D laser scanningdevice 134.

FIG. 3 illustrates the overall logical flow for performing thesegmentation in accordance with one or more embodiments of theinvention.

At step 302, the point cloud data is obtained.

At step 304, ground filtering is performed to filter out most of theterrain points.

At step 306, the remaining terrain points are further extracted based onthe local terrain surface property.

At step 308, non-ground points above the terrain are clustered intoindividual objects based on cell continuity.

At step 310, block-based segmentation is used to handle a large scene ofan urban environment and then combined.

Steps 304-310 are described in detail in the following sections.

Preprocessing—Ground Filtering

Step 304 is the pre-processing stage that filters out ground/terrainpoints. Ground point filtering has been researched for decades. Manyapproaches have been proposed that automatically and robustly filterground points and non-ground points. In the laser scanning datasegmentation process, it is desirable to involve ground points filteringonly as a preprocessing step. In this regard, efficiency and robustnessmay be considered important. In one or more embodiments of theinvention, the basic idea of the robust interpolation DTM determinationmethod [Kraus 2001, Briese 2002] may be used for ground filtering.However, embodiments of the invention may further simplify the method tomake it faster.

To enable the filtering, and based on the large amount of point clouddata for an urban environment, embodiments of the invention may firstsubdivide the point cloud data into different patches. Thereafter,ground point filtering is performed in each individual patch.

Patch Subdivision

The first step in subdividing the patches is determining the appropriatesize of the patches. The principle of patch size selection is that: (1)the terrain surface in a patch should not vary too much; and (2) thereis at least one ground point in each patch. Thus, each patch may beselected to have equal length and width. The length and width areadaptively set according to the mean distance of the point cloud data.

An efficient mechanism that avoids the construction of KNN (k-nearestneighbor) information may be employed to estimate the mean distance.Firstly, the entire point cloud data is roughly subdivided to make eachpatch have approximately 1000 points: the number of patches in x/y axisis calculated by √(N/1000), and the length and width of the roughpatches are calculated accordingly by the bounding box and the number ofpatches. Secondly, the patch with most points is selected. KNNinformation is generated only for the points in this patch. For eachpoint, the mean distance from this point to its six (6) neighbors iscalculated. The final mean distance is estimated to be the mean value ofthe mean distances of the points in this patch. In other words, a sampleset of points may be rendered and the distances of the neighbors for thepoints in the sample set are calculated and used to calculate the meandistance. Such a mean distance represents the resolution/density of thepoint cloud data.

After the mean distance is estimated, the patch length/width can be setto be a fixed multiple (e.g., 100) of the mean distance. The boundingbox of the point cloud data is subdivided into patches with equal lengthand width in both x and y axes but not in the z axis. Thereafter, thepoints are distributed into the patches.

Such a patch subdivision scheme not only reduces the peak memoryrequired but also makes the filtering process more efficient by reducingthe dimension of the points in a single run.

Ground Point Filtering in Individual Patches

Outlier Removal

Due to the complexity and dynamic of the urban environment, outliersmight exist in the point cloud data. To provide a more robust system,such outliers should be removed before performing substantive filtering.First, the nearest neighborhood information is constructed for thepoints in one patch. Thereafter, each point is examined. Considering theterrain surface is usually continuous and doesn't have very steepjumping, outliers may be filtered out based on the following criteria:if the height of a point is much higher or lower than the mean distanceof the point's neighborhood, then the point is regarded as an outlierpoint and will not participate in the ground filtering process. Suchoutlier removal serves to remove most of the outliers and reducedisturbances in the plane fitting phase of ground filtering process.

Classification of Ground Points and Non-Ground Points

To separate ground points and non-ground points (points on bushes,tresses, and buildings), the non-ground points may be eliminated in ahierarchical approach as described below and in the flow chart of FIG.4. Accordingly, FIG. 4 illustrates the logical flow for eliminatingnon-ground points in accordance with one or more embodiments of theinvention.

At step 402, a pyramid cell structure is constructed to achieve thehierarchical structure.

At step 404, the process begins for filtering out the non-ground pointsfrom coarse to fine. The level of the pyramid is adaptively adjustedaccording to the parameter setting of the patch size and the gridinterval to make sure that the number of cells in the coarsest levelranges from 4 to 8.

At step 406, the lowest points in each cell of the coarsest level areobtained as representative points of coarse terrain surface.

At step 408, these lowest points are used to compute a roughapproximation of the surface. In embodiments of the invention, a planarsurface may be used to interpolate the representative points.

At step 410, the oriented distances (residual) from the rough surfacesto the points in the cells are computed. Each z-measurement is weightedby its residual value. If the residual is larger than a certainthreshold, the points is filtered out as a non-ground point andeliminated from the surface interpolation. The points below or on theapproximation surface are assigned high weight (1.0) and the pointsabove the approximation surface are assigned weight according to thefollowing weight function:

${w_{i}} = \frac{1}{1 + \left( {a\left( {r_{i} - {g}} \right)^{b}} \right)}$

where r_(i) is the residual value; g is a shift value determinedautomatically from the distribution of the residual values; and thevalues a, b may be can set as described in [Briese 2002]. Thus, step 410utilizes a weighting function to determine the weight of each pointbased on the point's distance to the plane.

At steps 412-418, the above-ground points are iteratively filtered outand the approximate surface is updated. In this regard, the first stepis to update the approximation surface under the consideration of theweights. Points with high weights will attract the surface and pointswith low weights will have less influence on the surface interpolation.Some embodiments of the invention may utilize a robust linearinterpolation where Kriging interpolation is used to interpolate thesurface at step 414. Such interpolation involves extensive matrixcomputations and leads to low performance. Therefore, a weighted planefitting may be used to interpolate the surface. However, the use of aweighted plane fitting may lead to a negative effect on the groundfiltering result. Nonetheless, compared with the performance problem,some extent of accuracy loss may be worthwhile.

The process of surface interpolation (i.e., steps 412 and 414) may berepeated iteratively until all the non-ground points are filtered out ora maximum number of iterations is reached (i.e., via steps 416 and 418).Meanwhile, at step 414, the z-measurement of the center points in eachcell is saved for next level comparison.

At step 416, the cell of the next finer level of the pyramid isobtained. Once all the levels have been processed, (i.e., the finestlevel has been reached per step 418), the processing continues at step420. At step 420, the z-measurement of each point in a cell is comparedwith the z-measurement of the center point in the cell. If the signeddistance is above a distance threshold, then the point is classified asa non-ground point and eliminated from surface interpolation. Theremaining points in the cell are used for the lowest point calculation.Such processing is performed for each level (e.g., per step 404).

Accordingly, at step 420, all of the points are classified by comparingthe z-measurement with the interpolated z-measurement of the cell centerof the finest level. If the z-measurement of a point is a thresholdhigher than the interpolated z-measurement of the cell center point,then the point is classified as NON-GROUND point, otherwise, it isclassified as GROUND point.

The process is complete at step 422.

In view of FIG. 4, one may note that a weighted plane is used tointerpolate a surface. Thereafter, non-ground points are filtered outbased on the surface. Such a step is useful to remove the non-groundpoints and obtain the terrain. The terrain points can then beremoved—thereby removing the connection among objects so that theobjects can be properly segmented as described below (i.e., there are noextraneous points that link two objects together in a manner thatprohibits or makes it more difficult to perform segmentation).

Point Cloud Segmentation

Once ground points have been filtered out, the point cloud data may besegmented in an attempt to find/classify/separate the different objectsin the point cloud data.

The segmentation method used by embodiments of the invention belongs tothe category of surface discontinuity based segmentation. The thresholdfor discontinuity is defined to be ε. To solve the memory requirementproblem caused by large scale data and the density of laser scanningpoints from an urban environment, the whole scan may be divided intosmall blocks that are then each handled individually.

Patch Subdivision and Grid Construction

The entire point cloud is subdivided into blocks. Usually, the scan doesnot extend excessively in the upright (vertical) direction. Accordingly,the point cloud data may only be subdivided in the x/y directions.Firstly, the bounding box of the whole point cloud is subdivided alongthe x and y direction to form some rectangular blocks. The points arethen distributed to each block. The length of each block can beadaptively set to be a multiple of the predefined threshold ofdiscontinuity ε according to the memory capacity. For each block, theblock is organized in a 3D cubic grid structure. The cell dimension inthe grid structure is set to be the threshold of discontinuity ε. Thiskind of cell structure is a simple representation of point connectivity.An empty cell between two non-empty cells means a discontinuity existsbetween the points in the non-empty cells. It should also be noted thatsparse sampling of the environment will cause the increase of non-emptycells and thus resulting in over-segmentation of the point cloud data.

In view of the above, one may note that the same subdivision as thatused above (in the ground point filtering process) may be used.Alternatively, a new subdivision schema may be utilized. Thesubdivisions (i.e., into small blocks) are needed to reduce memoryconsumption and decrease the processing needed.

Accordingly, as described above, patches/blocks must be made the rightsize to make sure the size of the patch is not less than the density ofthe points (which may vary based on the height of the objects from theground) but are large enough to determine if there is a gap that definesan empty space. Thus, various distances may be used to measure the gapbetween the objects including the use of a threshold that may be basedon the mean distance (e.g., 10× or 20× the mean distance). If there is agap between the points that is greater than the threshold, the objectsmay be regarded as being disconnected.

Ground Points Removal

Even though most of the ground points have been filtered in thepreprocessing phase, there still might be some ground points remaining,which will affect the subsequent segmentation process. Therefore,embodiments of the invention may utilize an additional ground pointremoval process before the segmentation. FIG. 5 is a flow chartillustrating the additional ground point removal process in accordancewith one or more embodiments of the invention.

The second ground points removal process beings at step 502. At step504, a grid structure is built (i.e., for each patch). As describedabove, the grid structure is a 3D cubic grid structure that defines thecells in each rectangular block. With the grid structure, it is possibleto determine the height of points (with respect to the grid) todetermine whether they are ground points or not. For ground points, itmay be assumed that remaining terrain points in the cell will be planar.If it can be detected whether points in a cell are planar, then theheight of the cell to the bottom of the grid may also be determined. Inthis regard, if a non-empty cell exists beneath a cell being examined,then the cell being examined cannot be a ground cell because therecannot be any other points below the ground. Therefore, embodiments ofthe invention attempt to locate the lowest set of cells in a grid.

Step 506 determines whether all of the cells have been processed. Thus,the steps below step 506 are repeated for each cell.

At step 508, a determination is made regarding whether a cell is emptyor has been visited already. If the cell is empty or has already beenvisited/processed, the procedure proceeds to the next cell at step 506.If the cell is not empty and has not been visited/processed, adetermination is made regarding whether the cell is a ground cell atstep 510 and as set forth below in the “Ground Cell Detection” section.The results of, the determination are evaluated at step 512.

If the cell is not a ground cell, the process proceeds to the next cellby returning to step 506. If the cell is a ground cell, the cell isgrown to cluster other ground cells at step 514 and as set forth belowin the “Ground Cells Propagation” section. The ground cluster and groundplane are saved at step 516, and the process then proceeds to the nextcell at step 506.

The process is complete at step 518.

Ground Cell Detection

Based on the observation that the remaining ground points are usually insmall neighborhood regions, such points may be removed by clusteringadjacent cells once a ground cell is detected. A ground cell is detectedaccording to the steps set forth in the flow chart of FIG. 6 in view ofthe diagram of FIG. 7. In FIG. 7, there are non-empty cells 702 andempty cells 704 (i.e., empty/non-empty referring to whether there arepoints in a particular cell). In general, a ground cell should be one ofthe lowest non-empty neighboring cells. It may be assumed that therecannot be any other points below the ground. Thus, ground cells must bethe lowest set of cells in a grid. The process therefore evaluates eachcell and determines whether it is a ground cell or not. Suppose the cellbeing examined is named as cell C.

At step 602, the position of the lowest non-empty cell 706 in the samecolumn of cell C is found.

At step 604, the distance between the lowest non-empty cell and thebottom of the bounding box of the block is checked. If the distance islarger than a predefined threshold, then it means the cell might belongto an object far above the ground surface, such as a roof, thus thiscell is not used for further examination (i.e., it is not a ground cellat 606). Referring to FIG. 7, such a cell may be the non-ground cell708. Thus, the distance between the points in the cell 708 to the bottomof the grid bounding box may be calculated and then compared to adistance threshold. Such a threshold is used to determine whether theplane is raised from the horizontal plane (e.g., such as a roof, top ofa wall, etc.).

If the threshold distance is not exceeded, the cell is marked as apotential ground cell 710 at step 608.

At step 610, the continuous neighboring cells of the lowest non-emptycell in this vertical column are found. This kind of continuousneighboring cells is named as the lowest non-empty adjacent cell set.For example, in FIG. 7, the bottom two non-empty cells constitute alowest non-empty adjacent cell set. A check is then conducted (at step612) to determine if cell C is among the lowest non-empty adjacent cellset in the vertical column. This criterion is used to excludeoverhanging points such as leaves on the trees (i.e., outliers). If cellC is not in the lowest non-empty adjacent cell set in the column, thecell is not a ground cell at 606.

If cell C is in the lowest non-empty adjacent cell set in the column,then the thickness of the lowest non-empty adjacent cell set beingevaluated is examined at step 614. If the thickness of the lowestnon-empty adjacent cells is too large, cell C is rejected as a groundcell. This criterion is used to exclude points on a vertical surfacesuch as a building façade. In other words, if the cell is too thick,then the cells in this column are likely a building or other object thatshould not be considered a ground cell.

If the thickness is not too large, then the system evaluates the angleof the points in the cell. To perform such a calculation, a plane isdetermined (from the points in the cell) and the angle of the plane isdetermined. If the angle is too steep from the horizontal plane, itcannot be a ground cell. Accordingly, at step 616, the planarity of theplane is checked using the points in the lowest non-empty adjacentcells. If the points in the cells are not planar enough, then the cellcannot be a ground cell at 606. Further, if the plane is too steep fromthe horizontal plane, cell C is also not regarded as a ground cell. Ifthe angle is consistent with the angle of a ground cell, the systemmarks the cell as a ground cell at step 618.

If the cell C is marked as a ground cell, the fitting plane informationmay also be saved for subsequent processing.

Ground Cell Propagation

After a ground cell is detected, it is propagated to its adjacent cellsto cluster the ground cells. In other words, to further filter out theground cells, once one ground cell is detected, it ispropagated/clustered with nearby cells and examined to determine if thepoints in the cell belong to the ground plane.

FIG. 8 illustrates the logical flow for conducting ground cellpropagation in accordance with one or more embodiments of the invention.The process starts at step 802.

The propagation is conducted in a seed-filling manner. In this regard,to avoid a stack overflow problem, a non-recursive seed-filling methodmay be utilized. Steps 804-808 ensure that each neighboring cell of aground cell (referred to herein as cell C) is evaluated. In this regard,an evaluation regarding whether a neighboring cell is empty or has beenvisited in the past is conducted at step 806 for every 3*3*3 neighboringcells of cell C (per step 804). If the neighboring cell is not empty(i.e., contains points) and has not yet been visited, the neighbor cellis marked as being visited at step 808, and the process continues.

Step 810 ensures that the remaining steps are performed for every pointin an unvisited non-empty neighbor cell (e.g., via a “for” loop). Thus,each point in an unvisited neighboring cell of cell C is checked to seeif it is located on the plane. To perform such a determination, adistance d from each point to the plane is calculated.

A comparison between the distance d and a threshold value is conductedat step 812. If the distance from a point to the plane is more than thethreshold value, the point does not lie on the plane, and the processcontinues back at step 810.

If the distance from a point to the plane is less than the distancethreshold, then the point is added into the plane point set (i.e., ofthe ground cluster) at step 814. Once twenty (20) new points (or someother number of points) have been added to the ground cluster/planepoint set, the plane equation (i.e., the definition/representation ofthe plane) is updated at step 816.

A determination is made at step 818 regarding whether or not fiftypercent (50%) or more of the points in the cell have been added to theGROUND cluster (i.e., if more than 50% of the points in the cell areground points). If not, the process is finished at step 824 (e.g., forthe point being evaluated and if it is the last point, then the entireprocess is complete).

If most points (more than 50%) in a cell lie on the plane, this cell ismarked as a GROUND cell at step 820 and used for furthergrowing/propagating by returning to step 802.

This process iterates until no more points belonging to the plane can befound.

In view of the above, once the ground cells have been detected andpropagated, the system is more confident that the ground points havebeen removed/filtered out, and that the links between non-ground pointshave been cut-off/removed. Accordingly, the process may then continuewith the non-ground point segmentation.

Non-Ground Point Segmentation

It may be assumed that the cells of an object are continuous, i.e., eachcomponent cell is connected to some other (at least one) component cellof the object. The assumption is true for most of the objects existingin an urban environment. Therefore, after the removal of ground points,the connection among the objects disappears. Thus, the continuityinformation can be used as the sole criterion to segment the remainingnon-ground points into separate objects.

The segmentation starts from a random NONGROUND cell and propagates toits continuous neighboring cells using a non-recursive seed-fillingmethod as in the ground cell propagation (described above). To merge thesegmentation results from different small blocks, the cell informationfor cells on the border of the block are stored. Since the whole sceneis only subdivided in the x-y plane not in the z plane, it is onlynecessary to store the indices of objects containing boundary cells onthe four side faces—LEFT, RIGHT, FRONT and BACK. In this regard,embodiments of the invention first select a random non-empty cell, thenpropagate the non-empty cell to continuous neighboring cells, andlastly, if the non-empty cell is a border cell of one of the two or moreblocks, an object index for the object containing the border cell isstored in the side face cells of the block.

One may note that the methodology described herein is targeted forground-based laser scanning data and it is known that point densitydecreases with the height of objects from the ground. Therefore, in theNONGROUND cell growing process, the neighborhood range may be adaptivelyadjusted for objects above a certain distance from the ground to preventover-segmentation problems.

Combination of Block Segmentation

During the process of patch subdivision, one object might be subdividedinto several blocks. Therefore, after the segmentation in each block,the objects lying on two neighboring adjacent blocks need to becombined. The combination is checked both on adjacent block boundaryfaces and neighboring diagonal block corners.

As described above with respect to the non-ground points segmentation,objects lying on the side faces of the block have been saved. In thecombination process, objects of neighboring cells in different blocksshould belong to one object. The combination process begins with asearch for neighboring objects and then proceeds to combine the objects.In this regard, the search compares neighboring cells to each other tosee if they all have neighboring objects. Thereafter, if the cellscontain neighboring objects, the objects are combined into one object.

Neighboring Objects Search

The search for neighboring objects first searches for adjacent4—neighboring patches and then searches for diagonal patches. It may benoted, that the grid structure that is often used to subdivide the datawas not subdivided in the z-plane and was only subdivided in the x-yplane. Accordingly, embodiments of the invention may only be concernedwith the x-y direction while searching for neighboring objects. Further,boundary cells are only defined on four (4) planes—front, right, backand left (the top and bottom need not be examined).

Adjacent Patches

Of the four sides of the border faces, only two sides of a patch need tobe checked: (1) the RIGHT side of block (i, j) with the LEFT side ofblock (i+1, j) (see FIG. 9A); and (2) the BACK side of block (i,j) withthe FRONT side of block (i, j+1) (see FIG. 9B). Thus, FIGS. 9A and 9Billustrate merge objects lying on adjacent border faces of neighboringblocks in accordance with one or more embodiments of the invention.Overlapped cells on adjacent sides are checked to see if there areneighboring objects. As used herein, “neighboring objects” means onecell of the object is in the 3*3 neighboring cells of another object.Neighboring objects are merged into one object.

Diagonal Patches

For a block (i, j), it may only be necessary to check its BACK_LEFTcorner column with FRONT_RIGHT corner column of block (i−1, j+1) (seeFIG. 10A) and its BACK_RIGHT corner column with the FRONT_LEFT cornercolumn of patch (i+1, j+1). Each cell in the corner column is examinedto see if there are neighboring objects in its 3 neighboring cells.Neighboring objects are merged into one object (similar to that of theadjacent patches as described below). Thus, FIGS. 10A and 10B illustratemerge objects lying on diagonal corner columns of neighboring blocks inaccordance with one or more embodiments of the invention.

Object Combination

After two neighboring objects in different blocks are found, the pointsof the objects may not be immediately merged. Instead, an array M may beused to mark which object each object will be merged to. An object witha larger index is always merged to an object with a smaller index andthe value of the array is recursively updated to always store theclosest small index of its adjacent objects, forming an object link.Such actions facilitate the combination of the objects as the finalstep. Table 1 illustrates pseudo code that may be followed to set thearray and merge the objects.

TABLE 1 METHOD FOR OBJECT MERGE SETTING   void SetMergeToObject(M, s, t){   if (M[s] == s || M[s] == t)    M[s] = t;   else   {    if (M[s] > t)     SetMergeToObject(M, M[s], t);    else    {      r=vObjectMergeTo[s];      vObjectMergeTo[s] = t;      SetMergeToObject(M,t, r);     }    } }

As illustrated in Table 1, the array M is provided with two objectindices s and t that need to be merged. At the first step, if the arraylocation of s (i.e., M[s]) has the s or t index stored therein, the tindex is stored at the location. If the array location for s (i.e.,M[s]) has an index larger than t, the function is recursively called toreplace the larger index with the smaller index. Thereafter, the objectsare merged.

In other words, an array is used to store the combination results.

FIGS. 11A and 11B illustrate an example of the value change of array Mduring the object combination process. FIG. 11A illustrates the layoutof objects 2, 4, 7, and 9. FIG. 11B illustrates the status of the arrayduring the merge process. The initial status of the array M providesthat the index for each object is stored in the corresponding arraylocation.

During the first run, object 4 is linked to object 2. In other words,the two objects to be merged are objects 2 and 4. During the processing,the array entry for object 4 (i.e., M[4]) is set to 2.

In the second run, objects 9 and 2 are processed. During the processing,the value of the array for object 9 (i.e., M[9]) is changed from 9 to 4.Since the array value for object 4 (i.e., M[4]) has the value of 2,object 9 is linked to object 2 via the array.

In the third run, objects 9 and 7 are processed. During the processing,the lower array index (i.e., M[7]) points to the lowest value (i.e., 4taken from M[9]) and the array index for the higher object (i.e., M[9])is reset to point to object 7. Thus, object 7 is linked to object 9 and4.

In view of the above, one may note that since the object linkinformation is stored in descending order starting from the biggestindex, the process always merges an object with the bigger index to anobject with a smaller index, i.e. start from the object with biggestindex i and merge this object to object of MN. Thus, the object with thebiggest index, 9, is merged to the object identified at M[9]—i.e.,object 7. Similarly, the object with the next largest index, 7, ismerged to the object identified at M[7]—i.e., object 4. Likewise, object4 is merged to the object identified at M[4]—i.e., object 2. In thisway, all the linked objects will be combined to one object.

CONCLUSION

This concludes the description of the preferred embodiment of theinvention. The following describes some alternative embodiments foraccomplishing the present invention. For example, any type of computer,such as a mainframe, minicomputer, or personal computer, or computerconfiguration, such as a timesharing mainframe, local area network, orstandalone personal computer, could be used with the present invention.

In view of the above, a method, apparatus, article of manufacture, andcomputer program product provides the ability to segment point clouddata scanned by ground based laser scanners from an outdoor urbanenvironment. The workflow of this method is simple and easy to beimplemented. Further, embodiments of the invention can achieve a goodtradeoff between the accuracy and efficiency of processing. With thenovel block combination methodology, the segmentation can be processedblock by block, thus reducing the memory requirement. In addition, shapedescriptors of the resulting objects can be calculated for furtheranalysis such as classification. Moreover, the segmentation organizesthe whole point cloud data in terms of objects and thus is better forinterpretation and visualization.

The foregoing description of the preferred embodiment of the inventionhas been presented for the purposes of illustration and description. Itis not intended to be exhaustive or to limit the invention to theprecise form disclosed. Many modifications and variations are possiblein light of the above teaching. It is intended that the scope of theinvention be limited not by this detailed description, but rather by theclaims appended hereto.

REFERENCES

-   ALEKSEY GOLOVINSKIY AND THOMAS FUNKHOUSER. Min-Cut Based    Segmentation of Point Clouds. IEEE Workshop on Search in 3D and    Video (S3DV) at ICCV, September 2009.-   ALEKSEY GOLOVINSKIY, VLADIMIR G. KIM, AND THOMAS FUNKHOUSER.    SHAPE-BASED RECOGNITION OF 3D POINT CLOUDS IN URBAN ENVIRONMENTS.    INTERNATIONAL CONFERENCE ON COMPUTER VISION ICCV), September 2009.    BOYKOV, Y., AND FUNKA-LEA., G., Graph cuts and efficient n-d image    segmentation. IJCV, 70 (2): 109-131, 2006, 4.6.-   BRIESE, C., PFEIFER, N., & DORNINGER, P., 2002, Application of the    Robust Interpolation for DTM Determination, IAPRS, vol. XXXIII, pp.    55-61.-   DOUILLARD, B., UNDERWOOD, J. P., KUNTZ, N., VLASKINE, V., QUADROS,    A., MORTON, P. & FRENKEL, A. On the Segmentation of 3D LIDAR Point    Clouds, International Conference on Robotics and Automation (ICRA)    2011, 2011.-   J. HERNÁNDEZ AND B. MARCOTEGUI. Morphological Segmentation of    Building Facade Images. ICIP09—IEEE International Conference on    Image Processing 2009. Cairo, Egypt.-   KRAUS K., PFEIFER N., 2001: Advanced DTM Generation from LiDAR DATA.    International. Archives of Photogrammetry and Remote Sensing, Vol    XXXIV, 3/W4.-   MOOSMANN, F.; PINK, O.; STILLER, C., 2009, Segmentation of 3D lidar    data in non-flat urban environments using a local convexity    criterion, Intelligent Vehicles Symposium, 2009 IEEE.-   MELKUMYAN, NAREK, Surface-based Synthesis of 3D Maps for Outdoor    Unstructured Environments. PhD thesis, University of Sydney,    Australian Center for Filed Robotics, 2008.-   ZHU, X., ZHAO, H., LIU, Y., ZHAO, Y., AND ZHA, H., 2010,    Segmentation and classification of range image from an intelligent    vehicle in urban environment. In proceeding of the IEEE/RSJ    International Conference on Intelligent Robots and Systems (IROS),    1457-1462, 2010.

What is claimed is:
 1. A computer-implemented method for providingobject descriptors for objects in point cloud data for an urbanenvironment by segmenting the point cloud data in a computer that isprogrammed to perform: obtaining, using a ground-based laser scanner,the point cloud data, wherein the point cloud data is for the urbanenvironment; in a pre-process phase, filtering out, in the computer,terrain points from the point cloud data using ground filtering; afterthe pre-process phase, segmenting, in the computer, the point cloud datainto two or more blocks; combining, in the computer, objects that lie onneighboring adjacent blocks of the two or more blocks based on cellcontinuity; providing, in the computer, object descriptors for thecombined objects; utilizing, in the computer, the object descriptors toprocess the point cloud data, and facilitate and output a sceneinterpretation of the urban environment.
 2. The computer-implementedmethod of claim 1, wherein the ground filtering comprises: subdividingthe point cloud data into two or more patches; and filtering out theterrain points in each patch one patch at a time.
 3. Thecomputer-implemented method of claim 2, wherein: a size of each of thetwo or more patches is defined wherein each patch contains at least oneof the terrain points; and a length and width of each patch isadaptively set based on a mean distance of the point cloud data.
 4. Thecomputer-implemented method of claim 2, wherein the filtering out theterrain points in each patch one patch at a time comprises performingthe following for each patch: removing outlier points; classifyingground points and non-ground points; and filtering out the groundpoints.
 5. The computer-implemented method of claim 4, wherein theremoving outlier points comprises: constructing nearest neighborhoodinformation for all of the points in patch P; defining a mean distanceof the nearest neighborhood information for each point in patch P;examining each point in patch P; and removing the point as an outlierpoint if a height of the point is above or below a threshold amount fromthe mean distance.
 6. The computer-implemented method of claim 4,wherein the classifying ground points and non-ground points comprises:building a hierarchical structure of two or more cells for the patchbeing filtered, wherein the hierarchical structure ranges from coarse tofine; for each level from coarse to fine: obtaining lowest cell pointsof a current level as representative points of a coarse terrain surface;using the lowest cell points to calculate an approximate planar surface;calculating a weighted residual from the approximate planar surface toeach point in each cell; iteratively filtering out points that have aweighted residual above a threshold as non-ground points; updating theapproximate planar surface using the weighted residuals for points thathave not been filtered out; and interpolating a z-measurement of eachcell based on the updated approximate planar surface; and classifyingall of the remaining points by comparing the z-measurement of eachremaining point with an interpolated z-measurement of a cell center ofthe finest level, wherein if the z-measurement of each remaining pointis a threshold higher than the interpolated z-measurement of the cellcenter, then the point is classified as a non-ground point, and if not,the point is classified as a ground point.
 7. The computer-implementedmethod of claim 1, wherein the segmenting comprises removing additionalground points by: subdividing the point cloud data into the two or moreblocks; distributing points from the point cloud data to each of the twoor more blocks; organizing each block into a three-dimensional (3D)cubic grid structure having two or more cells; for each cell of the twoor more cells; determining if the cell is a ground cell based on agrounded plane; if the cell is a ground cell, propagating the cell toadjacent cells of the two or more cells in order to create a ground cellcluster; saving the ground cell cluster and the grounded plane; andfiltering out the ground cell cluster.
 8. The computer-implementedmethod of claim 1, wherein: the point cloud data is subdivided into thetwo or more blocks; points from the point cloud data are distributed toeach of the two or more blocks; each block is organized into athree-dimensional (3D) cubic grid structure having two or more cells;subsequent to filtering out the terrain points, remaining points of thepoint cloud data represent the objects; the segmenting the point clouddata comprises: selecting a random non-empty cell from the two or morecells; propagating the non-empty cell to continuous neighboring cells;and if the non-empty cell is a boundary cell of one of the two or moreblocks, storing an object index for the object containing the boundarycell.
 9. The computer-implemented method of claim 1, wherein thecombining comprises: searching for objects that lie on neighboringadjacent blocks by determining if one cell of a first object is in oneof 3*3 neighboring cells of a second object; and merging the firstobject and the second object when both the first object and the secondobject lie on neighboring adjacent blocks.
 10. The computer-implementedmethod of claim 9, wherein the merging comprises: utilizing an array tomark that the first object will be merged to the second object therebycreating an object link; and merging the first object into the secondobject based on the object link.
 11. An apparatus for providing objectdescriptors for objects in point cloud data for an urban environment bysegmenting the point cloud data in a computer system comprising: (a) acomputer having a memory; and (b) an application executing on thecomputer, wherein the application is configured to: (1) obtain, using aground-based laser scanner, the point cloud data, wherein the pointcloud data is for the urban environment; (2) in a pre-process phase,filter out terrain points from the point cloud data using groundfiltering; (3) after the pre-process phase, segment the point cloud datainto two or more blocks; (4) combine objects that lie on neighboringadjacent blocks of the two or more blocks based on cell continuity; (5)provide object descriptors for the combined objects; (6) utilize theobject descriptors to process the point cloud data, and facilitate anoutput a scene interpretation of the urban environment.
 12. Theapparatus of claim 11, wherein the ground filtering comprises:subdividing the point cloud data into two or more patches; and filteringout the terrain points in each patch one patch at a time.
 13. Theapparatus of claim 12, wherein: a size of each of the two or morepatches is defined wherein each patch contains at least one of theterrain points; and a length and width of each patch is adaptively setbased on a mean distance of the point cloud data.
 14. The apparatus ofclaim 12, wherein the application is configured to filter out theterrain points in each patch one patch at a time by performing thefollowing for each patch: removing outlier points; classifying groundpoints and non-ground points; and filtering out the ground points. 15.The apparatus of claim 14, wherein the removing outlier pointscomprises: constructing nearest neighborhood information for all of thepoints in patch P; defining a mean distance of the nearest neighborhoodinformation for each point in patch P; examining each point in patch P;and removing the point as an outlier point if a height of the point isabove or below a threshold amount from the mean distance.
 16. Theapparatus of claim 14, wherein the classifying ground points andnon-ground points comprises: building a hierarchical structure of two ormore cells for the patch being filtered, wherein the hierarchicalstructure ranges from coarse to fine; for each level from coarse tofine: obtaining lowest cell points of a current level as representativepoints of a coarse terrain surface; using the lowest cell points tocalculate an approximate planar surface; calculating a weighted residualfrom the approximate planar surface to each point in each cell;iteratively filtering out points that have a weighted residual above athreshold as non-ground points; updating the approximate planar surfaceusing the weighted residuals for points that have not been filtered out;and interpolating a z-measurement of each cell based on the updatedapproximate planar surface; and classifying all of the remaining pointsby comparing the z-measurement of each remaining point with aninterpolated z-measurement of a cell center of the finest level, whereinif the z-measurement of each remaining point is a threshold higher thanthe interpolated z-measurement of the cell center, then the point isclassified as a non-ground point, and if not, the point is classified asa ground point.
 17. The apparatus of claim 11, wherein the applicationis configured to segment by removing additional ground points by:subdividing the point cloud data into the two or more blocks;distributing points from the point cloud data to each of the two or moreblocks; organizing each block into a three-dimensional (3D) cubic gridstructure having two or more cells; for each cell of the two or morecells; determining if the cell is a ground cell based on a groundedplane; if the cell is a ground cell, propagating the cell to adjacentcells of the two or more cells in order to create a ground cell cluster;saving the ground cell cluster and the grounded plane; and filtering outthe ground cell cluster.
 18. The apparatus of claim 11, wherein: thepoint cloud data is subdivided into the two or more blocks; points fromthe point cloud data are distributed to each of the two or more blocks;each block is organized into a three-dimensional (3D) cubic gridstructure having two or more cells; subsequent to filtering out theterrain points, remaining points of the point cloud data represent theobjects; the segmenting the point cloud data comprises: selecting arandom non-empty cell from the two or more cells; propagating thenon-empty cell to continuous neighboring cells; and if the non-emptycell is a boundary cell of one of the two or more blocks, storing anobject index for the object containing the boundary cell.
 19. Theapparatus of claim 11, wherein the application is configured to combineby: searching for objects that lie on neighboring adjacent blocks bydetermining if one cell of a first object is in one of 3*3 neighboringcells of a second object; and merging the first object and the secondobject when both the first object and the second object lie onneighboring adjacent blocks.
 20. The apparatus of claim 19, wherein theapplication is configured to merge by: utilizing an array to mark thatthe first object will be merged to the second object thereby creating anobject link; and merging the first object into the second object basedon the object link.